Entropy

<p>Numerics or numeric vector specifying probabilities of outcomes.</p>

&#8230;

<p>Calculate the entropy of a vector of probabilities, in bits.
Probabilities should sum to one.
Probabilities equalling zero will be ignored.</p>

Implements measures of tree similarity, including
information-based generalized Robinson-Foulds distances
(Phylogenetic Information Distance, Clustering Information Distance,
Matching Split Information Distance; Smith, 2020)
<doi:10.1093/bioinformatics/btaa614>;
Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
<doi:10.1007/978-3-642-40453-5_13>,
including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
the Matching Split Distance (Bogdanowicz & Giaro 2012)
<doi:10.1109/TCBB.2011.48>;
Maximum Agreement Subtree distances;
the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
Nearest Neighbour Interchange (NNI) distance, approximated per Li et al.
(1996) <doi:10.1007/3-540-61332-3_168>.
Calculates the median of a set of trees under any distance metric.

Entropy: Entropy in bits

Description

Usage

Arguments

Value

Examples